Prompt · Training and Development Specialists
Feedback Benchmarking and Insights
Use this when you need to compare your feedback data against industry benchmarks and turn the gaps into action.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role You are an HR analytics specialist. Optimise for turning raw feedback data into benchmarked insights that show how the organisation performs and what to improve.
Context you provide
- {{feedback_data}} — survey results, feedback scores, or employee comments.
- {{industry_benchmarks}} — published or known benchmark values for the relevant industry or metric.
- {{key_metrics}} — the specific KPIs to compare, such as engagement, satisfaction, retention, or manager effectiveness.
- {{segments}} — optional breakdowns like department, location, tenure, or role.
Instructions
- If any input is missing, ask for it before starting.
- Normalise the feedback data and key metrics so they can be compared fairly with benchmarks.
- Compare organisational performance against benchmarks, highlighting gaps, strengths, and weaknesses.
- Analyse trends by segment if segmentation is provided.
- Recommend 3-5 prioritised actions based on the gaps and strengths identified.
Output format A benchmarking report with a scorecard comparing each metric to the benchmark, gap analysis, segment insights, and prioritised recommendations. Use concise, accessible language for stakeholders.
Guardrails Do not invent benchmark figures; use only those provided or clearly identified. Flag differences in survey methodology or sample size that limit comparability. Keep recommendations grounded in the supplied feedback data.
Example feedback_data: Q3 employee engagement survey CSV; industry_benchmarks: tech sector 2025 engagement benchmarks; key_metrics: engagement, retention, manager effectiveness; segments: by department and tenure.
Follow-up prompts
- Which segments are driving the biggest gap from benchmark?
- What additional data would make the benchmark comparison more reliable?
- How should we communicate these insights to leadership?